National Predictive Analytics Framework for Preventing Healthcare Fraud and Abuse
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Healthcare fraud and abuse remain critical challenges that undermine financial integrity and service delivery within health systems. This study developed a national predictive analytics framework to identify and prevent healthcare fraud using anomaly detection techniques. The analysis employed machine learning algorithms to evaluate claim patterns across different payer types and geographic regions. Results revealed that most healthcare claims exhibited normal behavioral patterns, while a small subset demonstrated significantly high anomaly scores, suggesting potential fraudulent activities. Commercial payers recorded the highest proportion of anomalies, followed by Medi-Cal, whereas Medicare showed the lowest frequency of irregular claims. Geographic analysis indicated that Los Angeles had the greatest concentration of anomalous records, highlighting a strong spatial clustering effect in high-volume urban regions. The correlation between Z-scores and anomaly scores confirmed the reliability of the model in detecting statistical deviations and behavioral inconsistencies. These findings emphasize the importance of integrating predictive analytics into healthcare oversight mechanisms to improve transparency, accountability, and operational efficiency. The study concludes that adopting data-driven fraud detection systems can significantly strengthen institutional capacity for proactive fraud prevention. Furthermore, the framework provides a scalable model that regulatory agencies and health insurers can adapt to monitor claim integrity, allocate resources efficiently, and sustain trust within the healthcare ecosystem.
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